
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "83"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 109 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 104 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.291707	7.648857	72.019619	73.510759	20.846042	25.918551	27.413121	28.240462	0.0556	0.0605	0.0025	1.396561	1.553363
2459593	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.585598	17.799130	74.347913	77.178101	29.626431	36.613987	13.105385	10.931875	0.0456	0.0506	0.0020	1.282415	1.409262
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.525215	1.543046	2.791336	2.449985	-0.124117	1.203450	-1.475025	0.251092	0.0677	0.0797	0.0071	nan	nan
2459591	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	346.794989	346.983297	inf	inf	71.822039	68.561181	876.123242	846.815585	nan	nan	nan	0.000000	0.000000
2459590	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.279974	22.635689	80.824682	83.508277	27.455693	33.638564	3.435792	2.827340	0.0518	0.0532	0.0003	1.435152	1.594982
2459589	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.673504	21.359477	75.124029	77.427290	16.508900	21.442508	18.042268	16.107877	0.0480	0.0525	0.0019	1.315755	1.465026
2459588	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.469005	16.421573	91.379399	94.757402	21.231113	24.319704	14.598519	13.170285	0.0495	0.0561	0.0046	1.283440	1.413727
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	20.975554	22.147795	121.905932	126.230027	6.215235	7.549682	10.396080	9.873083	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.429571	19.072833	28.947935	29.440011	9.441176	11.125402	5.004806	4.967149	0.0473	0.0523	0.0023	1.397065	1.813509
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.916314	25.283577	7.529655	7.954328	9.303985	10.146160	1.270933	1.996985	0.0640	0.0788	0.0112	0.000000	0.000000
2459584	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	26.963069	27.234414	98.682212	101.878814	15.909468	18.063169	38.429637	37.016305	0.0428	0.0463	0.0015	0.843645	0.830972
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.200767	19.528860	30.781847	31.719875	18.887871	22.595148	-0.209981	-0.145924	0.0453	0.0463	-0.0001	1.123820	1.106797
2459582	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.335230	16.148343	27.632763	28.792499	22.657493	27.364932	-0.133715	-0.112173	0.0404	0.0449	0.0018	1.284963	1.465197
2459581	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.131201	24.143492	32.055580	32.434640	17.765767	21.862372	0.098088	-0.111551	0.0463	0.0468	-0.0006	1.326443	1.411290
2459580	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.735919	17.550763	32.132357	33.243302	19.661821	23.090096	-0.371286	-0.270620	0.0471	0.0476	0.0002	0.000000	0.000000
2459579	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.649337	17.735092	36.406111	37.814571	22.135081	26.257002	0.752009	0.632555	0.0446	0.0453	-0.0004	1.123199	1.113472
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.000643	1.567170	0.499522	0.238761	-0.248047	1.107665	-0.012896	-0.649499	0.0589	0.0474	0.0019	nan	nan
2459577	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.621335	22.904566	29.746520	30.346340	20.083859	24.068242	-0.059404	-0.121263	0.0460	0.0470	-0.0007	1.332110	1.413170
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.768553	17.542988	23.647899	24.378969	18.450892	21.409616	0.543166	0.434719	0.0469	0.0472	-0.0005	1.402791	1.513347
2459575	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.158937	18.341727	23.473789	24.230053	14.891867	16.433085	7.129638	6.213367	0.0509	0.0528	-0.0000	1.525327	1.700916
2459574	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.899182	16.675681	22.498109	23.162841	20.168313	23.835166	0.994748	0.316035	0.0452	0.0468	-0.0006	1.385874	1.481076
2459573	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459572	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.396550	17.234413	22.584545	23.289912	15.272003	17.388545	1.452767	1.399186	0.0467	0.0481	-0.0006	1.383411	1.495262
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.491769	1.258910	0.309074	0.179378	-1.013271	-0.859244	-0.099252	-0.663493	0.0671	0.0608	0.0027	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.019282	20.375576	29.860660	30.357739	10.670851	10.807269	8.283986	9.026709	0.0573	0.0616	0.0006	1.334423	1.409690
2459566	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.432900	15.265094	29.519442	30.164866	18.354887	21.437804	-0.528251	-0.526428	0.0361	0.0407	0.0017	1.300146	1.499531
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	-0.457058	-0.457058	-0.461085	-0.461085	-0.863548	-0.863548	-0.922228	-0.922228	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.300725	14.345944	31.387809	32.140794	2.137471	2.192172	0.506815	0.407956	0.0464	0.0482	0.0002	1.329065	1.417619
2459563	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.834958	16.604997	60.060950	61.350362	19.129215	21.840184	1.427692	1.086725	0.0455	0.0486	0.0011	1.524007	1.756294
2459562	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.614282	10.785236	15.396541	15.815548	2.260797	2.589211	0.336882	0.299860	0.0469	0.0474	-0.0002	1.304021	1.363156
2459561	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.257665	11.214980	14.675469	15.050869	2.582876	2.829369	0.596335	0.419539	0.0480	0.0492	0.0001	1.299734	1.396165
2459560	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459559	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.487076	12.353065	16.448277	16.737689	2.414655	3.004097	0.568999	0.408483	0.0462	0.0489	0.0007	1.244125	1.338116
2459558	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.024966	17.741735	30.846575	32.039236	20.652547	22.619365	0.695208	0.585374	0.0467	0.0479	-0.0004	1.394822	1.481853
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0482	0.0405	0.0030	nan	nan
2459556	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.430970	18.383270	33.299919	34.692717	18.692988	20.443351	1.615136	1.606100	0.0513	0.0522	-0.0002	1.428275	1.543592
2459554	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0462	0.0474	-0.0002	nan	nan
2459553	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0475	0.0498	0.0009	nan	nan
2459552	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.162025	21.236809	44.912504	45.652627	28.503747	31.753941	2.671951	2.339589	0.0419	0.0427	0.0001	0.911084	0.910655
2459551	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.434033	10.495639	18.195186	18.552600	3.011613	3.960690	0.621016	0.456094	0.0473	0.0470	-0.0002	1.322651	1.400384
2459550	digital_ok	-	95.88%	95.88%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0699	0.0698	0.0087	nan	nan
2459549	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.814823	15.858907	33.757020	34.480367	20.098634	22.327091	0.468216	0.293145	0.0441	0.0447	-0.0003	1.397498	1.497791
2459542	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.471097	22.463647	68.898420	70.127734	1.082127	1.984464	0.935735	0.627149	0.0551	0.0652	0.0054	1.346262	1.448342
2459541	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.356765	11.823459	35.511779	36.686211	165.970221	181.460284	7.692570	8.509055	0.0460	0.0504	0.0020	1.377441	1.515979
2459540	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.992910	15.746346	15.342586	15.974854	4.197778	5.550600	0.436428	0.542358	0.0474	0.0511	0.0025	1.304985	1.399814
2459536	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	26.676733	24.407129	58.243875	59.212962	29.335599	31.447669	1.311212	0.750728	0.0377	0.0418	0.0018	0.885078	0.878166
2459535	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.580841	12.547451	14.282817	14.666150	2.850841	6.141285	-0.091838	-0.054880	0.0464	0.0524	0.0058	1.303120	1.422544
2459534	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.296933	12.784843	13.288566	13.560948	2.682163	4.429247	-0.188217	-0.370416	0.0407	0.0438	0.0014	1.312919	1.465167
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.807174	18.821483	31.470090	32.369266	17.351920	20.427166	0.454203	0.581275	0.0404	0.0435	0.0013	1.287524	1.398248
2459532	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.190189	22.506515	42.414212	43.936462	21.898705	25.521630	0.148784	0.117032	0.0439	0.0444	0.0008	1.136652	1.121838
2459530	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.892306	11.414713	13.426133	13.726326	3.244353	4.153251	0.217030	0.016419	0.0578	0.0532	0.0035	1.419229	1.526283
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	26.344106	26.398190	48.355118	49.625402	41.078917	35.706599	-0.288113	0.060401	0.0450	0.0802	0.0246	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	24.849576	22.439762	46.041957	46.360580	18.879477	30.778385	1.008715	0.732557	nan	nan	nan	1.639626	1.772512
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.466031	23.439375	45.550621	47.480488	11.076040	23.767363	0.491999	0.692624	0.0462	0.0471	-0.0003	0.930446	0.900068
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.802998	1.710401	0.329468	0.332160	-0.699969	-0.799804	-0.236955	-0.585116	0.0954	0.1088	0.0088	nan	nan
2459508	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.493624	29.190109	45.421380	47.034491	18.433322	15.868757	12.830348	14.769663	0.0435	0.0757	0.0210	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.543355	29.585674	41.640552	44.769716	6.709046	9.690486	0.140785	1.065258	0.0451	0.0486	0.0018	1.145298	1.152681
2459503	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	28.686547	29.334170	75.107861	76.944646	34.149031	33.669490	1.686838	2.129117	0.0632	0.0835	0.0162	0.000000	0.000000
2459501	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.797188	20.519764	50.210554	51.542985	28.961329	28.186933	1.409012	0.549494	0.0421	0.0627	0.0109	0.000000	0.000000
2459500	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.716111	23.721242	62.318252	63.621692	27.248042	27.367811	0.451625	0.673943	0.0416	0.0668	0.0152	0.000000	0.000000
2459499	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.084502	24.629357	80.123027	82.005480	27.378416	27.708532	3.000397	11.379265	0.0428	0.0846	0.0261	0.000000	0.000000
2459498	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.422648	25.034065	71.520149	73.138086	24.288212	24.416815	3.320312	3.536100	0.0693	0.0859	0.0062	0.000000	0.000000
2459497	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.121445	27.722415	80.605601	82.557178	31.570810	31.819458	2.206870	2.909247	0.0547	0.0707	0.0056	0.000000	0.000000
2459496	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.058862	29.744687	73.498741	75.005086	28.043226	28.695045	1.241495	1.652947	0.0517	0.0563	0.0002	0.000000	0.000000
2459495	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.513541	23.377853	68.595009	69.834728	24.092437	25.138977	1.869600	2.235948	0.0486	0.0535	0.0006	0.000000	0.000000
2459494	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.757572	20.589151	72.995490	74.434436	27.136985	28.297562	5.059154	5.332949	0.0395	0.0501	0.0037	0.000000	0.000000
2459492	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.731036	25.176373	49.791805	51.218431	24.710570	29.834235	3.784884	4.257902	0.0415	0.0477	0.0018	0.000000	0.000000
2459491	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.766628	22.374543	54.059474	56.175279	33.878149	44.727711	3.391616	12.148134	0.0406	0.0491	0.0045	0.000000	0.000000
2459490	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	28.896513	27.326235	56.610012	58.099347	23.060253	27.350685	3.499715	4.768316	0.0398	0.0457	0.0020	0.000000	0.000000
2459489	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.929394	22.581204	51.301947	52.884529	23.244562	29.402445	3.231900	6.989985	0.0383	0.0482	0.0026	0.000000	0.000000
2459488	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.387807	24.214557	43.008583	44.022789	25.465750	31.661624	4.865163	8.460594	0.0441	0.0486	0.0017	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 83 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 83, Node 7:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 83: 2459632

Antenna 83: 2459631

Antenna 83: 2459630

Antenna 83: 2459629

Antenna 83: 2459628

Antenna 83: 2459627

Antenna 83: 2459626

Antenna 83: 2459625

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Antenna 83: 2459623

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Antenna 83: 2459615

Antenna 83: 2459614

Antenna 83: 2459613

Antenna 83: 2459612

Antenna 83: 2459611

Antenna 83: 2459610

Antenna 83: 2459609

Antenna 83: 2459608

Antenna 83: 2459607

Antenna 83: 2459605

Antenna 83: 2459604

Antenna 83: 2459603

Antenna 83: 2459602

Antenna 83: 2459598

Antenna 83: 2459597

Antenna 83: 2459596

Antenna 83: 2459595

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Antenna 83: 2459583

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Antenna 83: 2459570

Antenna 83: 2459569

Antenna 83: 2459566

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Antenna 83: 2459563

Antenna 83: 2459562

Antenna 83: 2459561

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Antenna 83: 2459559

Antenna 83: 2459558

Antenna 83: 2459556

Antenna 83: 2459552

Antenna 83: 2459551

Antenna 83: 2459549

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶